Machine Learning And AI Are Not The Same: Here’s The Difference
When it comes to Big Data, these computer science terms are often used interchangeably, but they are not the same thing. While it may sound confusing, it is actually simple to differentiate the terms when you understand how they work together.
Here is the difference between AI and Machine Learning
Think of this as exactly what it sounds like, teaching a machine to learn. Machine learning uses programming through a thing called “neural networks.” This is where Machine Learning “learns” through training algorithms and determines the probable outcome of a situation. The process requires a human to program the information into the ML with data, hours of training and testing and fixing issues in the outcomes.
- Medical Diagnosis
- Software engineering
- Search engine optimization
The biggest example of ML is face detection image recognition. When shown enough photos of someone’s face from different angles, expressions, lighting, and more the machine can then start to recognize a person more efficiently and determine that it is likely that person in a photo based on characteristics. Google uses ML for optimizing advertisements as well and Netflix uses it to offer up recommendations for shows and movies.
The important thing to remember with ML is that it can only output what is input based on the large sets of data it is given. It can only check from what knowledge it has been “taught.” If that information is not available, it cannot create an outcome on its own. Therefore ML will go for the solution whether or not it is the most optimal solution.
AI can create outcomes on its own and do things that only a human could do. ML is a part of what helps AI by taking the data that it has been learned and then the AI takes that information along with past experiences and changes behaviour accordingly.
- Speech recognition
- Image classification
- Understanding natural language
When a machine completes a task based on a set of stipulated rules it has now become “artificially intelligent” such as moving objects and manipulating human behaviour by solving problems. The biggest example would be the image classification on something like Pinterest.
The goal of AI is to simulate natural intelligence to solve complex problems and increase the chance of success. AI will try and find the most optimal solution. It will use machine learning to reflect on the outcomes and optimize decision making based on observing its surrounding environment.
Think of the two as separate but hand in hand. They are both crucial to the future of technology and digital marketing and will be interesting to see how they grow together.